code: def adjust_learning_rate(self, optimizer, gamma, step, lr):
      lr = lr * (gamma**step)
      for param_group in optimizer.param_groups:
          param_group["lr"] = lr
      return param_group["lr"]
  
imports: import argparse
  import os
  import shutil
  import time
  import yaml
  import multiprocessing
  import numpy
  import torch
  import torch.nn
  import torch.optim
  import wandb
  from config.load_config import load_yaml, DotDict
  from data.dataset import SynthTextDataSet
  from loss.mseloss import Maploss_v2, Maploss_v3
  from model.craft import CRAFT
  from metrics.eval_det_iou import DetectionIoUEvaluator
  from eval import main_eval
  from utils.util import copyStateDict, save_parser
label_desc: Use image node_id 4 for calling the Trainer obj's underlying member method code with example usage: obj.adjust_learning_rate(optimizer, gamma, step, lr) and returns: param_group